Overview

Dataset statistics

Number of variables29
Number of observations437161
Missing cells517262
Missing cells (%)4.1%
Duplicate rows1924
Duplicate rows (%)0.4%
Total size in memory96.7 MiB
Average record size in memory232.0 B

Variable types

Numeric16
Text5
DateTime1
Categorical6
Unsupported1

Alerts

region has constant value "Bolivia"Constant
Dataset has 1924 (0.4%) duplicate rowsDuplicates
af_time_signature is highly imbalanced (87.1%)Imbalance
streams has 80101 (18.3%) missing valuesMissing
available_markets has 437161 (100.0%) missing valuesMissing
available_markets is an unsupported type, check if it needs cleaning or further analysisUnsupported
af_key has 43330 (9.9%) zerosZeros
af_instrumentalness has 230435 (52.7%) zerosZeros

Reproduction

Analysis started2024-07-15 20:36:27.852394
Analysis finished2024-07-15 20:37:26.417720
Duration58.57 seconds
Software versionydata-profiling v4.8.3
Download configurationconfig.json

Variables

id
Real number (ℝ)

Distinct217457
Distinct (%)49.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean612789.47
Minimum405514
Maximum810025
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.3 MiB
2024-07-15T16:37:26.522548image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum405514
5-th percentile433271
Q1524690
median627911
Q3699868
95-th percentile780498
Maximum810025
Range404511
Interquartile range (IQR)175178

Descriptive statistics

Standard deviation108241.45
Coefficient of variation (CV)0.17663726
Kurtosis-1.0703542
Mean612789.47
Median Absolute Deviation (MAD)86604
Skewness-0.12125134
Sum2.6788766 × 1011
Variance1.1716212 × 1010
MonotonicityIncreasing
2024-07-15T16:37:26.678328image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
721372 32
 
< 0.1%
721432 32
 
< 0.1%
721223 32
 
< 0.1%
711693 32
 
< 0.1%
721629 32
 
< 0.1%
732023 20
 
< 0.1%
634932 20
 
< 0.1%
733603 20
 
< 0.1%
632890 20
 
< 0.1%
634728 20
 
< 0.1%
Other values (217447) 436901
99.9%
ValueCountFrequency (%)
405514 1
< 0.1%
405515 2
< 0.1%
405516 2
< 0.1%
405517 2
< 0.1%
405521 2
< 0.1%
405524 2
< 0.1%
405528 2
< 0.1%
405533 1
< 0.1%
405534 2
< 0.1%
405535 1
< 0.1%
ValueCountFrequency (%)
810025 2
 
< 0.1%
810022 1
 
< 0.1%
810019 1
 
< 0.1%
810018 1
 
< 0.1%
810015 2
 
< 0.1%
810014 6
< 0.1%
810011 3
< 0.1%
810009 1
 
< 0.1%
810006 3
< 0.1%
810001 2
 
< 0.1%

title
Text

Distinct2384
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size3.3 MiB
2024-07-15T16:37:26.910039image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Length

Max length122
Median length70
Mean length17.337937
Min length2

Characters and Unicode

Total characters7579470
Distinct characters159
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique160 ?
Unique (%)< 0.1%

Sample

1st rowReggaetón Lento (Bailemos)
2nd rowOtra vez (feat. J Balvin)
3rd rowOtra vez (feat. J Balvin)
4th rowChantaje (feat. Maluma)
5th rowChantaje (feat. Maluma)
ValueCountFrequency (%)
113947
 
7.8%
remix 82615
 
5.6%
feat 65374
 
4.5%
la 32463
 
2.2%
me 25586
 
1.7%
with 17318
 
1.2%
te 17178
 
1.2%
de 15568
 
1.1%
en 14452
 
1.0%
que 13860
 
0.9%
Other values (3017) 1065145
72.8%
2024-07-15T16:37:27.293374image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1026345
 
13.5%
e 680822
 
9.0%
a 633872
 
8.4%
i 412557
 
5.4%
o 389076
 
5.1%
n 308622
 
4.1%
t 282164
 
3.7%
r 250059
 
3.3%
l 234964
 
3.1%
s 196136
 
2.6%
Other values (149) 3164853
41.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7579470
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1026345
 
13.5%
e 680822
 
9.0%
a 633872
 
8.4%
i 412557
 
5.4%
o 389076
 
5.1%
n 308622
 
4.1%
t 282164
 
3.7%
r 250059
 
3.3%
l 234964
 
3.1%
s 196136
 
2.6%
Other values (149) 3164853
41.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7579470
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1026345
 
13.5%
e 680822
 
9.0%
a 633872
 
8.4%
i 412557
 
5.4%
o 389076
 
5.1%
n 308622
 
4.1%
t 282164
 
3.7%
r 250059
 
3.3%
l 234964
 
3.1%
s 196136
 
2.6%
Other values (149) 3164853
41.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7579470
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1026345
 
13.5%
e 680822
 
9.0%
a 633872
 
8.4%
i 412557
 
5.4%
o 389076
 
5.1%
n 308622
 
4.1%
t 282164
 
3.7%
r 250059
 
3.3%
l 234964
 
3.1%
s 196136
 
2.6%
Other values (149) 3164853
41.8%

chart_rank
Real number (ℝ)

Distinct200
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean77.686818
Minimum1
Maximum200
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.3 MiB
2024-07-15T16:37:27.446156image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile6
Q127
median63
Q3124
95-th percentile184
Maximum200
Range199
Interquartile range (IQR)97

Descriptive statistics

Standard deviation58.027242
Coefficient of variation (CV)0.74693807
Kurtosis-0.99205696
Mean77.686818
Median Absolute Deviation (MAD)43
Skewness0.51522011
Sum33961647
Variance3367.1608
MonotonicityNot monotonic
2024-07-15T16:37:27.597481image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
16 4363
 
1.0%
18 4338
 
1.0%
10 4263
 
1.0%
11 4260
 
1.0%
4 4233
 
1.0%
9 4220
 
1.0%
12 4219
 
1.0%
1 4212
 
1.0%
21 4137
 
0.9%
8 4134
 
0.9%
Other values (190) 394782
90.3%
ValueCountFrequency (%)
1 4212
1.0%
2 4094
0.9%
3 3949
0.9%
4 4233
1.0%
5 4019
0.9%
6 4029
0.9%
7 4093
0.9%
8 4134
0.9%
9 4220
1.0%
10 4263
1.0%
ValueCountFrequency (%)
200 1352
0.3%
199 1405
0.3%
198 1395
0.3%
197 1355
0.3%
196 1286
0.3%
195 1308
0.3%
194 1309
0.3%
193 1426
0.3%
192 1319
0.3%
191 1426
0.3%
Distinct1826
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size3.3 MiB
Minimum2017-01-01 00:00:00
Maximum2021-12-31 00:00:00
2024-07-15T16:37:27.746217image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-15T16:37:27.896702image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

artist
Text

Distinct1685
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size3.3 MiB
2024-07-15T16:37:28.120493image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Length

Max length39
Median length33
Mean length9.3194544
Min length1

Characters and Unicode

Total characters4074102
Distinct characters136
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique113 ?
Unique (%)< 0.1%

Sample

1st rowCNCO
2nd rowJ Balvin
3rd rowZion & Lennox
4th rowShakira
5th rowMaluma
ValueCountFrequency (%)
bunny 21886
 
2.9%
bad 21830
 
2.9%
j 17957
 
2.4%
balvin 17824
 
2.4%
ozuna 13382
 
1.8%
sech 10927
 
1.4%
anuel 10368
 
1.4%
aa 10368
 
1.4%
maluma 9608
 
1.3%
alejandro 8675
 
1.1%
Other values (2385) 612442
81.1%
2024-07-15T16:37:28.486451image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 434663
 
10.7%
318106
 
7.8%
e 307606
 
7.6%
n 305827
 
7.5%
i 227612
 
5.6%
o 195757
 
4.8%
l 185442
 
4.6%
r 177498
 
4.4%
u 143908
 
3.5%
s 116732
 
2.9%
Other values (126) 1660951
40.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4074102
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 434663
 
10.7%
318106
 
7.8%
e 307606
 
7.6%
n 305827
 
7.5%
i 227612
 
5.6%
o 195757
 
4.8%
l 185442
 
4.6%
r 177498
 
4.4%
u 143908
 
3.5%
s 116732
 
2.9%
Other values (126) 1660951
40.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4074102
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 434663
 
10.7%
318106
 
7.8%
e 307606
 
7.6%
n 305827
 
7.5%
i 227612
 
5.6%
o 195757
 
4.8%
l 185442
 
4.6%
r 177498
 
4.4%
u 143908
 
3.5%
s 116732
 
2.9%
Other values (126) 1660951
40.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4074102
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 434663
 
10.7%
318106
 
7.8%
e 307606
 
7.6%
n 305827
 
7.5%
i 227612
 
5.6%
o 195757
 
4.8%
l 185442
 
4.6%
r 177498
 
4.4%
u 143908
 
3.5%
s 116732
 
2.9%
Other values (126) 1660951
40.8%

url
Text

Distinct2560
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size3.3 MiB
2024-07-15T16:37:28.660643image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Length

Max length53
Median length53
Mean length53
Min length53

Characters and Unicode

Total characters23169533
Distinct characters65
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique179 ?
Unique (%)< 0.1%

Sample

1st rowhttps://open.spotify.com/track/3AEZUABDXNtecAOSC1qTfo
2nd rowhttps://open.spotify.com/track/3QwBODjSEzelZyVjxPOHdq
3rd rowhttps://open.spotify.com/track/3QwBODjSEzelZyVjxPOHdq
4th rowhttps://open.spotify.com/track/6mICuAdrwEjh6Y6lroV2Kg
5th rowhttps://open.spotify.com/track/6mICuAdrwEjh6Y6lroV2Kg
ValueCountFrequency (%)
https://open.spotify.com/track/7g8yauqabmal0zwe7a2ijz 4781
 
1.1%
https://open.spotify.com/track/5stpvcrqb4qixbafp9e8lt 4605
 
1.1%
https://open.spotify.com/track/7fodjb7brqtgqh0hogw6xd 4438
 
1.0%
https://open.spotify.com/track/3v8ukqhek5zbkbb6d6ub8i 4182
 
1.0%
https://open.spotify.com/track/5id5b3dxjzhpcv9gzgyzze 4040
 
0.9%
https://open.spotify.com/track/7ansogdgtmjtapzbzcpho6 3500
 
0.8%
https://open.spotify.com/track/5q2jbcni4fcnglgpfxcv65 3475
 
0.8%
https://open.spotify.com/track/3ui9i3e1g1y5t3smfdgfgo 3400
 
0.8%
https://open.spotify.com/track/57ba26w9eoxx8bbungrnlv 3372
 
0.8%
https://open.spotify.com/track/4r8bjggjostswlxtkw8v7p 3330
 
0.8%
Other values (2550) 398038
91.1%
2024-07-15T16:37:28.936044image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
t 1912426
 
8.3%
/ 1748644
 
7.5%
p 1451402
 
6.3%
o 1449715
 
6.3%
c 1028699
 
4.4%
s 1013940
 
4.4%
. 874322
 
3.8%
e 598631
 
2.6%
a 594139
 
2.6%
h 591322
 
2.6%
Other values (55) 11906293
51.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 23169533
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
t 1912426
 
8.3%
/ 1748644
 
7.5%
p 1451402
 
6.3%
o 1449715
 
6.3%
c 1028699
 
4.4%
s 1013940
 
4.4%
. 874322
 
3.8%
e 598631
 
2.6%
a 594139
 
2.6%
h 591322
 
2.6%
Other values (55) 11906293
51.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 23169533
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
t 1912426
 
8.3%
/ 1748644
 
7.5%
p 1451402
 
6.3%
o 1449715
 
6.3%
c 1028699
 
4.4%
s 1013940
 
4.4%
. 874322
 
3.8%
e 598631
 
2.6%
a 594139
 
2.6%
h 591322
 
2.6%
Other values (55) 11906293
51.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 23169533
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
t 1912426
 
8.3%
/ 1748644
 
7.5%
p 1451402
 
6.3%
o 1449715
 
6.3%
c 1028699
 
4.4%
s 1013940
 
4.4%
. 874322
 
3.8%
e 598631
 
2.6%
a 594139
 
2.6%
h 591322
 
2.6%
Other values (55) 11906293
51.4%

region
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.3 MiB
Bolivia
437161 

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters3060127
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBolivia
2nd rowBolivia
3rd rowBolivia
4th rowBolivia
5th rowBolivia

Common Values

ValueCountFrequency (%)
Bolivia 437161
100.0%

Length

2024-07-15T16:37:29.065281image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-15T16:37:29.170152image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
bolivia 437161
100.0%

Most occurring characters

ValueCountFrequency (%)
i 874322
28.6%
B 437161
14.3%
o 437161
14.3%
l 437161
14.3%
v 437161
14.3%
a 437161
14.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3060127
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i 874322
28.6%
B 437161
14.3%
o 437161
14.3%
l 437161
14.3%
v 437161
14.3%
a 437161
14.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3060127
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i 874322
28.6%
B 437161
14.3%
o 437161
14.3%
l 437161
14.3%
v 437161
14.3%
a 437161
14.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3060127
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i 874322
28.6%
B 437161
14.3%
o 437161
14.3%
l 437161
14.3%
v 437161
14.3%
a 437161
14.3%

chart
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.3 MiB
top200
357060 
viral50
80101 

Length

Max length7
Median length6
Mean length6.18323
Min length6

Characters and Unicode

Total characters2703067
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowtop200
2nd rowtop200
3rd rowtop200
4th rowtop200
5th rowtop200

Common Values

ValueCountFrequency (%)
top200 357060
81.7%
viral50 80101
 
18.3%

Length

2024-07-15T16:37:29.278403image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-15T16:37:29.383016image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
top200 357060
81.7%
viral50 80101
 
18.3%

Most occurring characters

ValueCountFrequency (%)
0 794221
29.4%
t 357060
13.2%
o 357060
13.2%
p 357060
13.2%
2 357060
13.2%
v 80101
 
3.0%
i 80101
 
3.0%
r 80101
 
3.0%
a 80101
 
3.0%
l 80101
 
3.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2703067
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 794221
29.4%
t 357060
13.2%
o 357060
13.2%
p 357060
13.2%
2 357060
13.2%
v 80101
 
3.0%
i 80101
 
3.0%
r 80101
 
3.0%
a 80101
 
3.0%
l 80101
 
3.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2703067
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 794221
29.4%
t 357060
13.2%
o 357060
13.2%
p 357060
13.2%
2 357060
13.2%
v 80101
 
3.0%
i 80101
 
3.0%
r 80101
 
3.0%
a 80101
 
3.0%
l 80101
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2703067
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 794221
29.4%
t 357060
13.2%
o 357060
13.2%
p 357060
13.2%
2 357060
13.2%
v 80101
 
3.0%
i 80101
 
3.0%
r 80101
 
3.0%
a 80101
 
3.0%
l 80101
 
3.0%

trend
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.3 MiB
MOVE_DOWN
181447 
MOVE_UP
162264 
SAME_POSITION
69544 
NEW_ENTRY
23906 

Length

Max length13
Median length9
Mean length8.8939704
Min length7

Characters and Unicode

Total characters3888097
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSAME_POSITION
2nd rowSAME_POSITION
3rd rowSAME_POSITION
4th rowSAME_POSITION
5th rowSAME_POSITION

Common Values

ValueCountFrequency (%)
MOVE_DOWN 181447
41.5%
MOVE_UP 162264
37.1%
SAME_POSITION 69544
 
15.9%
NEW_ENTRY 23906
 
5.5%

Length

2024-07-15T16:37:29.498380image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-15T16:37:29.605187image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
move_down 181447
41.5%
move_up 162264
37.1%
same_position 69544
 
15.9%
new_entry 23906
 
5.5%

Most occurring characters

ValueCountFrequency (%)
O 664246
17.1%
E 461067
11.9%
_ 437161
11.2%
M 413255
10.6%
V 343711
8.8%
N 298803
7.7%
P 231808
 
6.0%
W 205353
 
5.3%
D 181447
 
4.7%
U 162264
 
4.2%
Other values (6) 488982
12.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3888097
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
O 664246
17.1%
E 461067
11.9%
_ 437161
11.2%
M 413255
10.6%
V 343711
8.8%
N 298803
7.7%
P 231808
 
6.0%
W 205353
 
5.3%
D 181447
 
4.7%
U 162264
 
4.2%
Other values (6) 488982
12.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3888097
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
O 664246
17.1%
E 461067
11.9%
_ 437161
11.2%
M 413255
10.6%
V 343711
8.8%
N 298803
7.7%
P 231808
 
6.0%
W 205353
 
5.3%
D 181447
 
4.7%
U 162264
 
4.2%
Other values (6) 488982
12.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3888097
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
O 664246
17.1%
E 461067
11.9%
_ 437161
11.2%
M 413255
10.6%
V 343711
8.8%
N 298803
7.7%
P 231808
 
6.0%
W 205353
 
5.3%
D 181447
 
4.7%
U 162264
 
4.2%
Other values (6) 488982
12.6%

streams
Real number (ℝ)

MISSING 

Distinct16384
Distinct (%)4.6%
Missing80101
Missing (%)18.3%
Infinite0
Infinite (%)0.0%
Mean4256.8504
Minimum1001
Maximum68209
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.3 MiB
2024-07-15T16:37:29.733197image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum1001
5-th percentile1200
Q12066
median2938
Q34867.25
95-th percentile11667.15
Maximum68209
Range67208
Interquartile range (IQR)2801.25

Descriptive statistics

Standard deviation3862.7271
Coefficient of variation (CV)0.90741436
Kurtosis18.886815
Mean4256.8504
Median Absolute Deviation (MAD)1140
Skewness3.2989307
Sum1.519951 × 109
Variance14920661
MonotonicityNot monotonic
2024-07-15T16:37:29.875169image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2786 163
 
< 0.1%
2214 158
 
< 0.1%
2724 157
 
< 0.1%
1021 156
 
< 0.1%
2754 155
 
< 0.1%
2182 154
 
< 0.1%
2435 153
 
< 0.1%
2469 150
 
< 0.1%
2730 148
 
< 0.1%
2291 147
 
< 0.1%
Other values (16374) 355519
81.3%
(Missing) 80101
 
18.3%
ValueCountFrequency (%)
1001 69
< 0.1%
1002 96
< 0.1%
1003 84
< 0.1%
1004 89
< 0.1%
1005 95
< 0.1%
1006 99
< 0.1%
1007 87
< 0.1%
1008 126
< 0.1%
1009 91
< 0.1%
1010 104
< 0.1%
ValueCountFrequency (%)
68209 2
< 0.1%
64255 1
< 0.1%
63590 1
< 0.1%
62765 1
< 0.1%
62750 2
< 0.1%
62483 1
< 0.1%
62437 2
< 0.1%
61086 1
< 0.1%
60847 2
< 0.1%
59830 2
< 0.1%
Distinct2560
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size3.3 MiB
2024-07-15T16:37:30.039371image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Length

Max length22
Median length22
Mean length22
Min length22

Characters and Unicode

Total characters9617542
Distinct characters62
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique179 ?
Unique (%)< 0.1%

Sample

1st row3AEZUABDXNtecAOSC1qTfo
2nd row3QwBODjSEzelZyVjxPOHdq
3rd row3QwBODjSEzelZyVjxPOHdq
4th row6mICuAdrwEjh6Y6lroV2Kg
5th row6mICuAdrwEjh6Y6lroV2Kg
ValueCountFrequency (%)
7g8yauqabmal0zwe7a2ijz 4781
 
1.1%
5stpvcrqb4qixbafp9e8lt 4605
 
1.1%
7fodjb7brqtgqh0hogw6xd 4438
 
1.0%
3v8ukqhek5zbkbb6d6ub8i 4182
 
1.0%
5id5b3dxjzhpcv9gzgyzze 4040
 
0.9%
7ansogdgtmjtapzbzcpho6 3500
 
0.8%
5q2jbcni4fcnglgpfxcv65 3475
 
0.8%
3ui9i3e1g1y5t3smfdgfgo 3400
 
0.8%
57ba26w9eoxx8bbungrnlv 3372
 
0.8%
4r8bjggjostswlxtkw8v7p 3330
 
0.8%
Other values (2550) 398038
91.1%
2024-07-15T16:37:30.309050image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
7 224644
 
2.3%
6 211495
 
2.2%
3 208519
 
2.2%
5 207779
 
2.2%
1 205821
 
2.1%
4 204919
 
2.1%
2 198418
 
2.1%
0 193646
 
2.0%
w 180242
 
1.9%
j 175554
 
1.8%
Other values (52) 7606505
79.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 9617542
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
7 224644
 
2.3%
6 211495
 
2.2%
3 208519
 
2.2%
5 207779
 
2.2%
1 205821
 
2.1%
4 204919
 
2.1%
2 198418
 
2.1%
0 193646
 
2.0%
w 180242
 
1.9%
j 175554
 
1.8%
Other values (52) 7606505
79.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 9617542
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
7 224644
 
2.3%
6 211495
 
2.2%
3 208519
 
2.2%
5 207779
 
2.2%
1 205821
 
2.1%
4 204919
 
2.1%
2 198418
 
2.1%
0 193646
 
2.0%
w 180242
 
1.9%
j 175554
 
1.8%
Other values (52) 7606505
79.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 9617542
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
7 224644
 
2.3%
6 211495
 
2.2%
3 208519
 
2.2%
5 207779
 
2.2%
1 205821
 
2.1%
4 204919
 
2.1%
2 198418
 
2.1%
0 193646
 
2.0%
w 180242
 
1.9%
j 175554
 
1.8%
Other values (52) 7606505
79.1%

album
Text

Distinct1805
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size3.3 MiB
2024-07-15T16:37:30.531536image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Length

Max length122
Median length72
Mean length16.115017
Min length1

Characters and Unicode

Total characters7044857
Distinct characters165
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique87 ?
Unique (%)< 0.1%

Sample

1st rowPrimera Cita
2nd rowMotivan2
3rd rowMotivan2
4th rowEl Dorado
5th rowEl Dorado
ValueCountFrequency (%)
remix 57560
 
4.5%
44739
 
3.5%
feat 43591
 
3.4%
the 23126
 
1.8%
el 17138
 
1.3%
la 15267
 
1.2%
de 14477
 
1.1%
with 13588
 
1.1%
te 12561
 
1.0%
me 11649
 
0.9%
Other values (2671) 1019870
80.1%
2024-07-15T16:37:30.916213image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
836405
 
11.9%
e 581403
 
8.3%
a 523019
 
7.4%
i 383819
 
5.4%
o 372998
 
5.3%
n 268019
 
3.8%
r 246107
 
3.5%
t 239782
 
3.4%
l 226727
 
3.2%
s 190105
 
2.7%
Other values (155) 3176473
45.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7044857
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
836405
 
11.9%
e 581403
 
8.3%
a 523019
 
7.4%
i 383819
 
5.4%
o 372998
 
5.3%
n 268019
 
3.8%
r 246107
 
3.5%
t 239782
 
3.4%
l 226727
 
3.2%
s 190105
 
2.7%
Other values (155) 3176473
45.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7044857
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
836405
 
11.9%
e 581403
 
8.3%
a 523019
 
7.4%
i 383819
 
5.4%
o 372998
 
5.3%
n 268019
 
3.8%
r 246107
 
3.5%
t 239782
 
3.4%
l 226727
 
3.2%
s 190105
 
2.7%
Other values (155) 3176473
45.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7044857
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
836405
 
11.9%
e 581403
 
8.3%
a 523019
 
7.4%
i 383819
 
5.4%
o 372998
 
5.3%
n 268019
 
3.8%
r 246107
 
3.5%
t 239782
 
3.4%
l 226727
 
3.2%
s 190105
 
2.7%
Other values (155) 3176473
45.1%

popularity
Real number (ℝ)

Distinct96
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean74.924129
Minimum0
Maximum100
Zeros2901
Zeros (%)0.7%
Negative0
Negative (%)0.0%
Memory size3.3 MiB
2024-07-15T16:37:31.066483image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile60
Q171
median76
Q381
95-th percentile89
Maximum100
Range100
Interquartile range (IQR)10

Descriptive statistics

Standard deviation12.154379
Coefficient of variation (CV)0.16222249
Kurtosis15.762823
Mean74.924129
Median Absolute Deviation (MAD)5
Skewness-3.0089474
Sum32753907
Variance147.72892
MonotonicityNot monotonic
2024-07-15T16:37:31.207335image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
74 31665
 
7.2%
76 30093
 
6.9%
71 27385
 
6.3%
77 26062
 
6.0%
79 23618
 
5.4%
75 19900
 
4.6%
73 17666
 
4.0%
78 16352
 
3.7%
72 14837
 
3.4%
83 14017
 
3.2%
Other values (86) 215566
49.3%
ValueCountFrequency (%)
0 2901
0.7%
1 1097
 
0.3%
2 229
 
0.1%
3 232
 
0.1%
4 631
 
0.1%
5 2
 
< 0.1%
6 323
 
0.1%
8 26
 
< 0.1%
10 29
 
< 0.1%
11 4
 
< 0.1%
ValueCountFrequency (%)
100 952
 
0.2%
99 268
 
0.1%
98 340
 
0.1%
97 1193
 
0.3%
96 1006
 
0.2%
95 1102
 
0.3%
94 3865
0.9%
93 1887
0.4%
92 2997
0.7%
91 1978
0.5%

duration_ms
Real number (ℝ)

Distinct2380
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean231711.27
Minimum41454
Maximum581728
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.3 MiB
2024-07-15T16:37:31.344505image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum41454
5-th percentile163333
Q1198499
median219520
Q3254773
95-th percentile333800
Maximum581728
Range540274
Interquartile range (IQR)56274

Descriptive statistics

Standard deviation52855.257
Coefficient of variation (CV)0.22810827
Kurtosis1.6580623
Mean231711.27
Median Absolute Deviation (MAD)27496
Skewness1.1268661
Sum1.0129513 × 1011
Variance2.7936782 × 109
MonotonicityNot monotonic
2024-07-15T16:37:31.490466image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
360960 4781
 
1.1%
249520 4605
 
1.1%
305962 4438
 
1.0%
216066 4229
 
1.0%
301714 4185
 
1.0%
417920 4182
 
1.0%
286506 3500
 
0.8%
307910 3475
 
0.8%
230280 3400
 
0.8%
307435 3372
 
0.8%
Other values (2370) 396994
90.8%
ValueCountFrequency (%)
41454 6
 
< 0.1%
62422 1
 
< 0.1%
66226 4
 
< 0.1%
80117 22
< 0.1%
80453 6
 
< 0.1%
80927 43
< 0.1%
86739 24
< 0.1%
87533 1
 
< 0.1%
87706 45
< 0.1%
94245 3
 
< 0.1%
ValueCountFrequency (%)
581728 2
 
< 0.1%
544640 3
 
< 0.1%
544626 5
 
< 0.1%
522000 3
 
< 0.1%
520786 7
 
< 0.1%
498960 10
 
< 0.1%
493795 8
 
< 0.1%
486840 59
< 0.1%
478173 7
 
< 0.1%
460573 1
 
< 0.1%

explicit
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.3 MiB
0.0
307971 
1.0
129190 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1311483
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 307971
70.4%
1.0 129190
29.6%

Length

2024-07-15T16:37:31.615664image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-15T16:37:31.712298image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 307971
70.4%
1.0 129190
29.6%

Most occurring characters

ValueCountFrequency (%)
0 745132
56.8%
. 437161
33.3%
1 129190
 
9.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1311483
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 745132
56.8%
. 437161
33.3%
1 129190
 
9.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1311483
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 745132
56.8%
. 437161
33.3%
1 129190
 
9.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1311483
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 745132
56.8%
. 437161
33.3%
1 129190
 
9.9%

release_date
Real number (ℝ)

Distinct974
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.5293265 × 1012
Minimum-8.836128 × 1011
Maximum1.6245792 × 1012
Zeros16
Zeros (%)< 0.1%
Negative149
Negative (%)< 0.1%
Memory size3.3 MiB
2024-07-15T16:37:31.835623image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum-8.836128 × 1011
5-th percentile1.4020128 × 1012
Q11.5259968 × 1012
median1.5609888 × 1012
Q31.5873408 × 1012
95-th percentile1.6149024 × 1012
Maximum1.6245792 × 1012
Range2.508192 × 1012
Interquartile range (IQR)6.1344 × 1010

Descriptive statistics

Standard deviation1.5318892 × 1011
Coefficient of variation (CV)0.10016758
Kurtosis38.439529
Mean1.5293265 × 1012
Median Absolute Deviation (MAD)3.09312 × 1010
Skewness-5.62225
Sum6.6856188 × 1017
Variance2.3466846 × 1022
MonotonicityNot monotonic
2024-07-15T16:37:31.985353image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.5574464 × 101211749
 
2.7%
1.570752 × 101210361
 
2.4%
1.555632 × 10128009
 
1.8%
1.5991776 × 10127698
 
1.8%
1.56168 × 10127686
 
1.8%
1.582848 × 10127641
 
1.7%
1.5447456 × 10126089
 
1.4%
1.5725664 × 10125885
 
1.3%
1.5761952 × 10125766
 
1.3%
1.5235776 × 10125145
 
1.2%
Other values (964) 361132
82.6%
ValueCountFrequency (%)
-8.836128 × 10113
< 0.1%
-3.89232 × 10111
 
< 0.1%
-3.854304 × 10112
 
< 0.1%
-3.812832 × 10116
< 0.1%
-3.471552 × 10115
< 0.1%
-3.156192 × 10117
< 0.1%
-2.524608 × 10111
 
< 0.1%
-2.325024 × 10117
< 0.1%
-2.209248 × 10115
< 0.1%
-1.92672 × 10112
 
< 0.1%
ValueCountFrequency (%)
1.6245792 × 10121669
0.4%
1.6244928 × 1012624
 
0.1%
1.6239744 × 1012636
 
0.1%
1.6237152 × 10121030
0.2%
1.6233696 × 101270
 
< 0.1%
1.6232832 × 101223
 
< 0.1%
1.6231968 × 101298
 
< 0.1%
1.6227648 × 1012638
 
0.1%
1.6226784 × 1012426
 
0.1%
1.622592 × 101211
 
< 0.1%

available_markets
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing437161
Missing (%)100.0%
Memory size3.3 MiB

af_danceability
Real number (ℝ)

Distinct608
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.73121607
Minimum0.078299999
Maximum0.98000002
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.3 MiB
2024-07-15T16:37:32.124314image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0.078299999
5-th percentile0.51599997
Q10.67299998
median0.74699998
Q30.80800003
95-th percentile0.87900001
Maximum0.98000002
Range0.90170002
Interquartile range (IQR)0.13500005

Descriptive statistics

Standard deviation0.1104272
Coefficient of variation (CV)0.15101856
Kurtosis1.5290495
Mean0.73121607
Median Absolute Deviation (MAD)0.066999972
Skewness-1.037148
Sum319659.15
Variance0.012194166
MonotonicityNot monotonic
2024-07-15T16:37:32.267237image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.8069999814 7590
 
1.7%
0.8169999719 6108
 
1.4%
0.824000001 5411
 
1.2%
0.6880000234 5336
 
1.2%
0.7929999828 5303
 
1.2%
0.7269999981 4823
 
1.1%
0.6470000148 4747
 
1.1%
0.7860000134 4738
 
1.1%
0.6389999986 4730
 
1.1%
0.6539999843 4705
 
1.1%
Other values (598) 383670
87.8%
ValueCountFrequency (%)
0.0782999992 59
< 0.1%
0.150000006 41
< 0.1%
0.1679999977 7
 
< 0.1%
0.1790000051 7
 
< 0.1%
0.1809999943 14
 
< 0.1%
0.2070000023 24
< 0.1%
0.2090000063 4
 
< 0.1%
0.2150000036 6
 
< 0.1%
0.2179999948 5
 
< 0.1%
0.2230000049 1
 
< 0.1%
ValueCountFrequency (%)
0.9800000191 79
 
< 0.1%
0.9670000076 90
 
< 0.1%
0.9639999866 356
0.1%
0.9629999995 52
 
< 0.1%
0.9599999785 4
 
< 0.1%
0.9559999704 55
 
< 0.1%
0.949000001 36
 
< 0.1%
0.9480000138 24
 
< 0.1%
0.9470000267 2
 
< 0.1%
0.9449999928 79
 
< 0.1%

af_energy
Real number (ℝ)

Distinct688
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.68781507
Minimum0.0561
Maximum0.995
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.3 MiB
2024-07-15T16:37:32.408567image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0.0561
5-th percentile0.442
Q10.62599999
median0.70099998
Q30.78200001
95-th percentile0.86799997
Maximum0.995
Range0.93890001
Interquartile range (IQR)0.15600002

Descriptive statistics

Standard deviation0.1304793
Coefficient of variation (CV)0.18970114
Kurtosis1.3791856
Mean0.68781507
Median Absolute Deviation (MAD)0.076999962
Skewness-0.90350672
Sum300685.92
Variance0.017024848
MonotonicityNot monotonic
2024-07-15T16:37:32.546279image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.6719999909 9228
 
2.1%
0.6750000119 7793
 
1.8%
0.8080000281 5838
 
1.3%
0.6650000215 5710
 
1.3%
0.6370000243 5165
 
1.2%
0.6470000148 5069
 
1.2%
0.7120000124 4938
 
1.1%
0.6200000048 4841
 
1.1%
0.6610000134 4555
 
1.0%
0.8149999976 4335
 
1.0%
Other values (678) 379689
86.9%
ValueCountFrequency (%)
0.0560999997 7
 
< 0.1%
0.0808999985 9
 
< 0.1%
0.0828000009 2
 
< 0.1%
0.0847000033 8
 
< 0.1%
0.1110000014 119
< 0.1%
0.1120000035 6
 
< 0.1%
0.1150000021 29
 
< 0.1%
0.1230000034 13
 
< 0.1%
0.125 9
 
< 0.1%
0.1289999932 7
 
< 0.1%
ValueCountFrequency (%)
0.9950000048 122
 
< 0.1%
0.9929999709 7
 
< 0.1%
0.9890000224 109
 
< 0.1%
0.9779999852 4
 
< 0.1%
0.9769999981 2
 
< 0.1%
0.9750000238 42
 
< 0.1%
0.97299999 49
 
< 0.1%
0.9720000029 946
0.2%
0.9700000286 6
 
< 0.1%
0.9689999819 6
 
< 0.1%

af_key
Real number (ℝ)

ZEROS 

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.5658327
Minimum0
Maximum11
Zeros43330
Zeros (%)9.9%
Negative0
Negative (%)0.0%
Memory size3.3 MiB
2024-07-15T16:37:32.658483image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median6
Q39
95-th percentile11
Maximum11
Range11
Interquartile range (IQR)7

Descriptive statistics

Standard deviation3.7179581
Coefficient of variation (CV)0.66799674
Kurtosis-1.3319725
Mean5.5658327
Median Absolute Deviation (MAD)4
Skewness-0.054058373
Sum2433165
Variance13.823213
MonotonicityNot monotonic
2024-07-15T16:37:32.762568image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
1 58391
13.4%
11 54855
12.5%
0 43330
9.9%
6 43168
9.9%
5 40512
9.3%
7 35849
8.2%
10 32992
7.5%
8 32864
7.5%
2 32821
7.5%
9 32611
7.5%
Other values (2) 29768
6.8%
ValueCountFrequency (%)
0 43330
9.9%
1 58391
13.4%
2 32821
7.5%
3 12187
 
2.8%
4 17581
 
4.0%
5 40512
9.3%
6 43168
9.9%
7 35849
8.2%
8 32864
7.5%
9 32611
7.5%
ValueCountFrequency (%)
11 54855
12.5%
10 32992
7.5%
9 32611
7.5%
8 32864
7.5%
7 35849
8.2%
6 43168
9.9%
5 40512
9.3%
4 17581
 
4.0%
3 12187
 
2.8%
2 32821
7.5%

af_loudness
Real number (ℝ)

Distinct2152
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-5.0986925
Minimum-27.666
Maximum0.175
Zeros0
Zeros (%)0.0%
Negative437009
Negative (%)> 99.9%
Memory size3.3 MiB
2024-07-15T16:37:32.887419image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum-27.666
5-th percentile-8.8100004
Q1-5.98
median-4.7729998
Q3-3.8150001
95-th percentile-2.5880001
Maximum0.175
Range27.841
Interquartile range (IQR)2.165

Descriptive statistics

Standard deviation2.0066997
Coefficient of variation (CV)-0.39357143
Kurtosis4.8715639
Mean-5.0986925
Median Absolute Deviation (MAD)1.1059997
Skewness-1.5184133
Sum-2228949.5
Variance4.0268437
MonotonicityNot monotonic
2024-07-15T16:37:33.030224image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-4.802999973 5293
 
1.2%
-8.029000282 4821
 
1.1%
-5.980000019 4605
 
1.1%
-5.02699995 4438
 
1.0%
-2.453999996 4229
 
1.0%
-3.701999903 4185
 
1.0%
-3.444999933 4182
 
1.0%
-7.125 3633
 
0.8%
-5.020999908 3500
 
0.8%
-4.024000168 3475
 
0.8%
Other values (2142) 394800
90.3%
ValueCountFrequency (%)
-27.66600037 5
 
< 0.1%
-23.02300072 7
 
< 0.1%
-22.60199928 5
 
< 0.1%
-22.08499908 7
 
< 0.1%
-20.7329998 6
 
< 0.1%
-20.61100006 59
< 0.1%
-19.34600067 9
 
< 0.1%
-18.43499947 6
 
< 0.1%
-17.83200073 9
 
< 0.1%
-17.64599991 45
< 0.1%
ValueCountFrequency (%)
0.174999997 152
 
< 0.1%
-0.5149999857 277
 
0.1%
-0.7390000224 238
 
0.1%
-0.7760000229 26
 
< 0.1%
-1.121999979 8
 
< 0.1%
-1.282999992 1
 
< 0.1%
-1.343999982 1
 
< 0.1%
-1.351999998 42
 
< 0.1%
-1.386999965 3372
0.8%
-1.406000018 57
 
< 0.1%

af_mode
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.3 MiB
1.0
241471 
0.0
195690 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1311483
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row1.0
3rd row1.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
1.0 241471
55.2%
0.0 195690
44.8%

Length

2024-07-15T16:37:33.156762image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-15T16:37:33.255871image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
1.0 241471
55.2%
0.0 195690
44.8%

Most occurring characters

ValueCountFrequency (%)
0 632851
48.3%
. 437161
33.3%
1 241471
 
18.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1311483
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 632851
48.3%
. 437161
33.3%
1 241471
 
18.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1311483
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 632851
48.3%
. 437161
33.3%
1 241471
 
18.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1311483
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 632851
48.3%
. 437161
33.3%
1 241471
 
18.4%

af_speechiness
Real number (ℝ)

Distinct892
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.12505159
Minimum0.0231
Maximum0.884
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.3 MiB
2024-07-15T16:37:33.382229image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0.0231
5-th percentile0.0332
Q10.0561
median0.0929
Q30.164
95-th percentile0.324
Maximum0.884
Range0.8609
Interquartile range (IQR)0.1079

Descriptive statistics

Standard deviation0.092966092
Coefficient of variation (CV)0.74342192
Kurtosis1.1914892
Mean0.12505159
Median Absolute Deviation (MAD)0.0447
Skewness1.3002535
Sum54667.677
Variance0.0086426942
MonotonicityNot monotonic
2024-07-15T16:37:33.527369image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.1000000015 6700
 
1.5%
0.1529999971 5374
 
1.2%
0.200000003 5175
 
1.2%
0.2140000015 4912
 
1.1%
0.0366999991 4824
 
1.1%
0.2199999988 4788
 
1.1%
0.3109999895 4605
 
1.1%
0.112999998 4323
 
1.0%
0.0741000026 4092
 
0.9%
0.123999998 3845
 
0.9%
Other values (882) 388523
88.9%
ValueCountFrequency (%)
0.0230999999 7
 
< 0.1%
0.0231999997 822
0.2%
0.0234999992 73
 
< 0.1%
0.0240000002 259
 
0.1%
0.0242999997 562
0.1%
0.0247000009 7
 
< 0.1%
0.0249000005 13
 
< 0.1%
0.0250000004 135
 
< 0.1%
0.0251000002 9
 
< 0.1%
0.0252999999 73
 
< 0.1%
ValueCountFrequency (%)
0.8840000033 11
 
< 0.1%
0.77700001 82
 
< 0.1%
0.648999989 7
 
< 0.1%
0.6069999933 4
 
< 0.1%
0.5299999714 52
 
< 0.1%
0.4850000143 2
 
< 0.1%
0.4830000103 3
 
< 0.1%
0.4810000062 84
 
< 0.1%
0.4790000021 32
 
< 0.1%
0.4670000076 268
0.1%

af_acousticness
Real number (ℝ)

Distinct1335
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.23921697
Minimum1.05 × 10-5
Maximum0.99000001
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.3 MiB
2024-07-15T16:37:33.667800image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum1.05 × 10-5
5-th percentile0.0141
Q10.076499999
median0.18000001
Q30.361
95-th percentile0.63200003
Maximum0.99000001
Range0.98998951
Interquartile range (IQR)0.2845

Descriptive statistics

Standard deviation0.20558297
Coefficient of variation (CV)0.85939958
Kurtosis0.72157331
Mean0.23921697
Median Absolute Deviation (MAD)0.12360001
Skewness1.115789
Sum104576.33
Variance0.042264356
MonotonicityNot monotonic
2024-07-15T16:37:33.815379image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.1609999985 5422
 
1.2%
0.753000021 4789
 
1.1%
0.175999999 4726
 
1.1%
0.1800000072 4632
 
1.1%
0.4709999859 4631
 
1.1%
0.1209999993 4613
 
1.1%
0.1099999994 4522
 
1.0%
0.125 4301
 
1.0%
0.0258000009 4229
 
1.0%
0.0846000016 4185
 
1.0%
Other values (1325) 391111
89.5%
ValueCountFrequency (%)
1.05 × 10-53
 
< 0.1%
2.67 × 10-57
 
< 0.1%
3.62 × 10-52
 
< 0.1%
4.16 × 10-519
< 0.1%
5.54 × 10-514
< 0.1%
8.66 × 10-511
< 0.1%
9.22 × 10-54
 
< 0.1%
0.000109 2
 
< 0.1%
0.000155 16
< 0.1%
0.000164 3
 
< 0.1%
ValueCountFrequency (%)
0.9900000095 7
 
< 0.1%
0.9890000224 38
 
< 0.1%
0.9850000143 433
0.1%
0.9810000062 78
 
< 0.1%
0.9779999852 119
 
< 0.1%
0.9679999948 29
 
< 0.1%
0.9649999738 32
 
< 0.1%
0.9629999995 110
 
< 0.1%
0.9620000124 24
 
< 0.1%
0.9570000172 2
 
< 0.1%

af_instrumentalness
Real number (ℝ)

ZEROS 

Distinct1029
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0054577476
Minimum0
Maximum0.96399999
Zeros230435
Zeros (%)52.7%
Negative0
Negative (%)0.0%
Memory size3.3 MiB
2024-07-15T16:37:33.962829image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31.69 × 10-5
95-th percentile0.00184
Maximum0.96399999
Range0.96399999
Interquartile range (IQR)1.69 × 10-5

Descriptive statistics

Standard deviation0.054889019
Coefficient of variation (CV)10.057083
Kurtosis178.19186
Mean0.0054577476
Median Absolute Deviation (MAD)0
Skewness12.828585
Sum2385.9144
Variance0.0030128044
MonotonicityNot monotonic
2024-07-15T16:37:34.107316image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 230435
52.7%
5.32 × 10-64783
 
1.1%
2.27 × 10-64438
 
1.0%
0.000289 4185
 
1.0%
1.28 × 10-54182
 
1.0%
8.33 × 10-63372
 
0.8%
1.14 × 10-63157
 
0.7%
1.2 × 10-62838
 
0.6%
4.21 × 10-62797
 
0.6%
1.57 × 10-52728
 
0.6%
Other values (1019) 174246
39.9%
ValueCountFrequency (%)
0 230435
52.7%
1.01 × 10-643
 
< 0.1%
1.02 × 10-6165
 
< 0.1%
1.03 × 10-66
 
< 0.1%
1.04 × 10-633
 
< 0.1%
1.05 × 10-616
 
< 0.1%
1.06 × 10-627
 
< 0.1%
1.07 × 10-625
 
< 0.1%
1.08 × 10-658
 
< 0.1%
1.1 × 10-61021
 
0.2%
ValueCountFrequency (%)
0.9639999866 7
 
< 0.1%
0.9610000253 135
< 0.1%
0.9499999881 6
 
< 0.1%
0.9480000138 2
 
< 0.1%
0.9250000119 7
 
< 0.1%
0.9240000248 24
 
< 0.1%
0.9169999957 6
 
< 0.1%
0.9150000215 76
< 0.1%
0.9100000262 111
< 0.1%
0.90200001 2
 
< 0.1%

af_liveness
Real number (ℝ)

Distinct844
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.15866542
Minimum0.018999999
Maximum0.98000002
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.3 MiB
2024-07-15T16:37:34.245975image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0.018999999
5-th percentile0.0537
Q10.086599998
median0.118
Q30.192
95-th percentile0.37
Maximum0.98000002
Range0.96100002
Interquartile range (IQR)0.1054

Descriptive statistics

Standard deviation0.11339166
Coefficient of variation (CV)0.71465896
Kurtosis5.8895597
Mean0.15866542
Median Absolute Deviation (MAD)0.039000004
Skewness2.1100668
Sum69362.332
Variance0.012857669
MonotonicityNot monotonic
2024-07-15T16:37:34.392066image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.1040000021 7449
 
1.7%
0.1080000028 6442
 
1.5%
0.1070000008 6288
 
1.4%
0.1199999973 6036
 
1.4%
0.0821999982 5955
 
1.4%
0.1059999987 5838
 
1.3%
0.1180000007 5787
 
1.3%
0.1570000052 5613
 
1.3%
0.1430000067 5399
 
1.2%
0.1010000035 5349
 
1.2%
Other values (834) 377005
86.2%
ValueCountFrequency (%)
0.0189999994 7
 
< 0.1%
0.0207000002 513
0.1%
0.0215000007 212
 
< 0.1%
0.0219000001 8
 
< 0.1%
0.0240000002 708
0.2%
0.0242999997 13
 
< 0.1%
0.0247000009 2
 
< 0.1%
0.0251000002 40
 
< 0.1%
0.0258000009 8
 
< 0.1%
0.0280000009 8
 
< 0.1%
ValueCountFrequency (%)
0.9800000191 2
 
< 0.1%
0.976000011 6
 
< 0.1%
0.9679999948 7
 
< 0.1%
0.9629999995 3
 
< 0.1%
0.9620000124 7
 
< 0.1%
0.9549999833 48
< 0.1%
0.9539999962 1
 
< 0.1%
0.9509999752 2
 
< 0.1%
0.9350000024 7
 
< 0.1%
0.9309999943 2
 
< 0.1%

af_valence
Real number (ℝ)

Distinct853
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.62355664
Minimum0.032000001
Maximum0.98199999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.3 MiB
2024-07-15T16:37:34.543986image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0.032000001
5-th percentile0.236
Q10.49000001
median0.66600001
Q30.77399999
95-th percentile0.91900003
Maximum0.98199999
Range0.94999999
Interquartile range (IQR)0.28399998

Descriptive statistics

Standard deviation0.20255273
Coefficient of variation (CV)0.32483453
Kurtosis-0.34716374
Mean0.62355664
Median Absolute Deviation (MAD)0.12800002
Skewness-0.53933695
Sum272594.64
Variance0.041027607
MonotonicityNot monotonic
2024-07-15T16:37:34.945299image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.7269999981 5225
 
1.2%
0.5799999833 5203
 
1.2%
0.8109999895 5159
 
1.2%
0.3429999948 4605
 
1.1%
0.6769999862 4550
 
1.0%
0.4420000017 4201
 
1.0%
0.6800000072 4193
 
1.0%
0.7059999704 4091
 
0.9%
0.7670000196 3947
 
0.9%
0.6079999804 3941
 
0.9%
Other values (843) 392046
89.7%
ValueCountFrequency (%)
0.0320000015 30
 
< 0.1%
0.0359999985 135
< 0.1%
0.0377999991 13
 
< 0.1%
0.0379999988 62
< 0.1%
0.0392000005 19
 
< 0.1%
0.0392999984 128
< 0.1%
0.0395000018 5
 
< 0.1%
0.0498999991 32
 
< 0.1%
0.0507999994 22
 
< 0.1%
0.0516999997 20
 
< 0.1%
ValueCountFrequency (%)
0.9819999933 4
 
< 0.1%
0.9779999852 1
 
< 0.1%
0.976000011 10
 
< 0.1%
0.9739999771 14
 
< 0.1%
0.97299999 262
0.1%
0.9710000157 1
 
< 0.1%
0.9689999819 1
 
< 0.1%
0.9679999948 47
 
< 0.1%
0.9670000076 611
0.1%
0.9660000205 100
 
< 0.1%

af_tempo
Real number (ℝ)

Distinct2362
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean123.6728
Minimum57.966999
Maximum211.974
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.3 MiB
2024-07-15T16:37:35.079987image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum57.966999
5-th percentile81.906998
Q194.017998
median105.095
Q3167.897
95-th percentile180.11301
Maximum211.974
Range154.007
Interquartile range (IQR)73.879005

Descriptive statistics

Standard deviation35.490133
Coefficient of variation (CV)0.28696797
Kurtosis-1.272112
Mean123.6728
Median Absolute Deviation (MAD)17.066002
Skewness0.5383722
Sum54064927
Variance1259.5496
MonotonicityNot monotonic
2024-07-15T16:37:35.221045image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
170.0110016 4781
 
1.1%
181.8569946 4605
 
1.1%
167.9689941 4452
 
1.0%
113.3580017 4438
 
1.0%
176.0749969 4229
 
1.0%
96.50700378 4182
 
1.0%
91.97299957 3701
 
0.8%
172.0200043 3500
 
0.8%
169.8009949 3475
 
0.8%
120.0250015 3400
 
0.8%
Other values (2352) 396398
90.7%
ValueCountFrequency (%)
57.96699905 14
 
< 0.1%
62.42599869 7
 
< 0.1%
62.48400116 10
 
< 0.1%
62.52000046 6
 
< 0.1%
62.94800186 14
 
< 0.1%
64.17700195 42
 
< 0.1%
64.93399811 143
< 0.1%
65.375 5
 
< 0.1%
66.30200195 5
 
< 0.1%
67.00299835 4
 
< 0.1%
ValueCountFrequency (%)
211.973999 39
 
< 0.1%
209.0800018 4
 
< 0.1%
207.7980042 63
 
< 0.1%
203.7449951 20
 
< 0.1%
201.7389984 4
 
< 0.1%
201.6289978 2
 
< 0.1%
200.9429932 3
 
< 0.1%
200.1560059 412
0.1%
200.0359955 28
 
< 0.1%
199.8659973 16
 
< 0.1%

af_time_signature
Categorical

IMBALANCE 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.3 MiB
4.0
421941 
3.0
 
7921
5.0
 
6802
1.0
 
497

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1311483
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4.0
2nd row4.0
3rd row4.0
4th row4.0
5th row4.0

Common Values

ValueCountFrequency (%)
4.0 421941
96.5%
3.0 7921
 
1.8%
5.0 6802
 
1.6%
1.0 497
 
0.1%

Length

2024-07-15T16:37:35.350406image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-15T16:37:35.453483image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
4.0 421941
96.5%
3.0 7921
 
1.8%
5.0 6802
 
1.6%
1.0 497
 
0.1%

Most occurring characters

ValueCountFrequency (%)
. 437161
33.3%
0 437161
33.3%
4 421941
32.2%
3 7921
 
0.6%
5 6802
 
0.5%
1 497
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1311483
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
. 437161
33.3%
0 437161
33.3%
4 421941
32.2%
3 7921
 
0.6%
5 6802
 
0.5%
1 497
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1311483
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
. 437161
33.3%
0 437161
33.3%
4 421941
32.2%
3 7921
 
0.6%
5 6802
 
0.5%
1 497
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1311483
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
. 437161
33.3%
0 437161
33.3%
4 421941
32.2%
3 7921
 
0.6%
5 6802
 
0.5%
1 497
 
< 0.1%

Interactions

2024-07-15T16:37:21.504692image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-15T16:36:47.860839image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-15T16:36:50.248141image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-15T16:36:52.482933image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-15T16:36:54.739056image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-15T16:36:56.963484image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-15T16:36:59.314615image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-15T16:37:01.502732image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-15T16:37:03.685453image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-15T16:37:05.953029image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-15T16:37:08.095520image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-15T16:37:10.247205image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-15T16:37:12.406728image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-15T16:37:14.741389image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-15T16:37:17.100718image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-15T16:37:19.293320image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-15T16:37:21.655384image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-15T16:36:48.032837image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-15T16:36:50.411141image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-15T16:36:52.626934image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-15T16:36:54.886060image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-15T16:36:57.123454image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-15T16:36:59.467614image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-15T16:37:01.683926image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-15T16:37:03.828422image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-15T16:37:06.098056image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-15T16:37:08.240489image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-15T16:37:10.411379image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-15T16:37:12.549741image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-15T16:37:14.884383image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-15T16:37:17.257805image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-15T16:37:19.448556image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-15T16:37:21.797686image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-15T16:36:48.184062image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-15T16:36:50.550144image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-15T16:36:52.764932image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-15T16:36:55.024059image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-15T16:36:57.266092image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-15T16:36:59.605584image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-15T16:37:01.821925image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-15T16:37:03.962453image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-15T16:37:06.234062image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-15T16:37:08.393326image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-15T16:37:10.548377image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-15T16:37:12.687728image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-15T16:37:15.021918image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-15T16:37:17.414021image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-15T16:37:19.591492image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-15T16:37:21.944735image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-15T16:36:48.336614image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-15T16:36:50.692140image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-15T16:36:52.901153image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-15T16:36:55.162081image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-15T16:36:57.422060image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-15T16:36:59.747605image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-15T16:37:01.960922image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-15T16:37:04.104422image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-15T16:37:06.382413image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-15T16:37:08.531011image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-15T16:37:10.685376image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-15T16:37:12.827730image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-15T16:37:15.159922image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-15T16:37:17.553783image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-15T16:37:19.740242image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-15T16:37:22.078886image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-15T16:36:48.482584image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-15T16:36:50.827172image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-15T16:36:53.035604image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-15T16:36:55.302058image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-15T16:36:57.554060image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-15T16:36:59.883580image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-15T16:37:02.099925image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-15T16:37:04.232421image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-15T16:37:06.512410image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-15T16:37:08.662011image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-15T16:37:10.824406image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-15T16:37:12.964008image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-15T16:37:15.297091image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-15T16:37:17.687428image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-15T16:37:19.872422image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-15T16:37:22.216554image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-15T16:36:48.631379image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-15T16:36:50.964141image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-15T16:36:53.170604image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-15T16:36:55.453261image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-15T16:36:57.688091image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-15T16:37:00.019782image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-15T16:37:02.231926image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-15T16:37:04.365435image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-15T16:37:06.643382image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-15T16:37:08.795220image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-15T16:37:10.957409image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-15T16:37:13.098008image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-15T16:37:15.453091image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-15T16:37:17.828355image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-15T16:37:20.009612image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-15T16:37:22.356229image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-15T16:36:48.770381image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-15T16:36:51.101463image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-15T16:36:53.307604image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-15T16:36:55.595260image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-15T16:36:57.825093image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-15T16:37:00.156801image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-15T16:37:02.364150image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-15T16:37:04.506452image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-15T16:37:06.775412image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-15T16:37:08.928509image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-15T16:37:11.090379image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-15T16:37:13.234761image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-15T16:37:15.591121image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-15T16:37:17.969569image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-15T16:37:20.141028image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-15T16:37:22.499199image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-15T16:36:48.904378image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-15T16:36:51.234466image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-15T16:36:53.445601image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-15T16:36:55.724260image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-15T16:36:57.958092image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-15T16:37:00.288802image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-15T16:37:02.496139image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-15T16:37:04.632422image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-15T16:37:06.903403image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-15T16:37:09.057729image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-15T16:37:11.219379image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-15T16:37:13.364458image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-15T16:37:15.721313image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-15T16:37:18.099007image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-15T16:37:20.272400image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-15T16:37:22.629489image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-15T16:36:49.040382image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-15T16:36:51.373706image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-15T16:36:53.573276image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-15T16:36:55.850263image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-15T16:36:58.091091image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-15T16:37:00.429836image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
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2024-07-15T16:37:09.183759image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-15T16:37:11.345377image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
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2024-07-15T16:37:15.853038image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
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2024-07-15T16:37:20.422146image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-15T16:37:22.762812image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
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2024-07-15T16:36:55.983291image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
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2024-07-15T16:37:00.559031image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
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2024-07-15T16:37:04.882776image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
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2024-07-15T16:37:09.311728image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
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2024-07-15T16:37:13.815652image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-15T16:37:15.987036image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-15T16:37:18.364608image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-15T16:37:20.552322image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-15T16:37:22.899523image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-15T16:36:49.312347image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-15T16:36:51.650546image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-15T16:36:53.827612image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-15T16:36:56.117263image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-15T16:36:58.369064image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-15T16:37:00.690444image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-15T16:37:02.881777image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-15T16:37:05.011773image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-15T16:37:07.304837image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-15T16:37:09.448728image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-15T16:37:11.608726image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-15T16:37:13.949683image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-15T16:37:16.117065image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-15T16:37:18.505649image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-15T16:37:20.686700image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-15T16:37:23.031408image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-15T16:36:49.455352image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-15T16:36:51.783546image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-15T16:36:54.052404image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-15T16:36:56.245803image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-15T16:36:58.510060image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-15T16:37:00.820415image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-15T16:37:03.010043image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-15T16:37:05.139567image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-15T16:37:07.439807image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-15T16:37:09.576760image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-15T16:37:11.734758image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-15T16:37:14.073652image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-15T16:37:16.254038image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-15T16:37:18.633156image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-15T16:37:20.812646image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-15T16:37:23.166854image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-15T16:36:49.605351image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-15T16:36:51.919574image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-15T16:36:54.183404image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-15T16:36:56.418988image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-15T16:36:58.645060image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-15T16:37:00.953732image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-15T16:37:03.141046image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-15T16:37:05.267594image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-15T16:37:07.569804image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-15T16:37:09.708760image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-15T16:37:11.863586image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-15T16:37:14.204652image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-15T16:37:16.403066image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-15T16:37:18.764381image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-15T16:37:20.953071image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-15T16:37:23.533744image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-15T16:36:49.742799image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-15T16:36:52.058546image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-15T16:36:54.311404image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-15T16:36:56.551292image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-15T16:36:58.788061image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-15T16:37:01.089727image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-15T16:37:03.271185image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-15T16:37:05.417567image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-15T16:37:07.699490image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-15T16:37:09.840759image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-15T16:37:11.992582image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-15T16:37:14.335964image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-15T16:37:16.559690image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-15T16:37:18.893007image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-15T16:37:21.084201image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-15T16:37:23.668857image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-15T16:36:49.881799image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-15T16:36:52.192574image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-15T16:36:54.455058image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-15T16:36:56.684292image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-15T16:36:58.929063image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-15T16:37:01.220731image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-15T16:37:03.420153image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-15T16:37:05.684596image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-15T16:37:07.828490image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-15T16:37:09.972728image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-15T16:37:12.122007image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-15T16:37:14.473387image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-15T16:37:16.754848image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-15T16:37:19.022499image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-15T16:37:21.216443image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-15T16:37:23.803894image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-15T16:36:50.019144image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-15T16:36:52.327574image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-15T16:36:54.594058image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-15T16:36:56.815483image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-15T16:36:59.176268image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-15T16:37:01.350730image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-15T16:37:03.548153image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-15T16:37:05.814061image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-15T16:37:07.958493image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-15T16:37:10.105728image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-15T16:37:12.251727image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-15T16:37:14.602387image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-15T16:37:16.952427image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-15T16:37:19.153999image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-15T16:37:21.348803image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Missing values

2024-07-15T16:37:24.069498image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
A simple visualization of nullity by column.
2024-07-15T16:37:24.899329image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

idtitlechart_rankchart_dateartisturlregioncharttrendstreamstrack_idalbumpopularityduration_msexplicitrelease_dateavailable_marketsaf_danceabilityaf_energyaf_keyaf_loudnessaf_modeaf_speechinessaf_acousticnessaf_instrumentalnessaf_livenessaf_valenceaf_tempoaf_time_signature
0405514.0Reggaetón Lento (Bailemos)1.02017-01-01CNCOhttps://open.spotify.com/track/3AEZUABDXNtecAOSC1qTfoBoliviatop200SAME_POSITION6784.03AEZUABDXNtecAOSC1qTfoPrimera Cita74.0222560.00.01.472170e+12NaN0.7610.8384.0-3.0730.00.05020.400000.0000000.1760.71093.9739994.0
1405515.0Otra vez (feat. J Balvin)2.02017-01-01J Balvinhttps://open.spotify.com/track/3QwBODjSEzelZyVjxPOHdqBoliviatop200SAME_POSITION5748.03QwBODjSEzelZyVjxPOHdqMotivan274.0209453.00.01.475194e+12NaN0.8320.77210.0-5.4291.00.10000.055900.0004860.4400.70496.0159994.0
2405515.0Otra vez (feat. J Balvin)2.02017-01-01Zion & Lennoxhttps://open.spotify.com/track/3QwBODjSEzelZyVjxPOHdqBoliviatop200SAME_POSITION5748.03QwBODjSEzelZyVjxPOHdqMotivan274.0209453.00.01.475194e+12NaN0.8320.77210.0-5.4291.00.10000.055900.0004860.4400.70496.0159994.0
3405516.0Chantaje (feat. Maluma)3.02017-01-01Shakirahttps://open.spotify.com/track/6mICuAdrwEjh6Y6lroV2KgBoliviatop200SAME_POSITION5506.06mICuAdrwEjh6Y6lroV2KgEl Dorado76.0195840.00.01.495757e+12NaN0.8520.7738.0-2.9210.00.07760.187000.0000300.1590.907102.0339974.0
4405516.0Chantaje (feat. Maluma)3.02017-01-01Malumahttps://open.spotify.com/track/6mICuAdrwEjh6Y6lroV2KgBoliviatop200SAME_POSITION5506.06mICuAdrwEjh6Y6lroV2KgEl Dorado76.0195840.00.01.495757e+12NaN0.8520.7738.0-2.9210.00.07760.187000.0000300.1590.907102.0339974.0
5405517.0Vente Pa' Ca (feat. Maluma)4.02017-01-01Malumahttps://open.spotify.com/track/7DM4BPaS7uofFul3ywMe46Boliviatop200MOVE_UP4804.07DM4BPaS7uofFul3ywMe46Vente Pa' Ca (feat. Maluma)73.0259195.00.01.474502e+12NaN0.6630.92011.0-4.0700.00.22600.004310.0000170.1010.53399.9349984.0
6405517.0Vente Pa' Ca (feat. Maluma)4.02017-01-01Ricky Martinhttps://open.spotify.com/track/7DM4BPaS7uofFul3ywMe46Boliviatop200MOVE_UP4804.07DM4BPaS7uofFul3ywMe46Vente Pa' Ca (feat. Maluma)73.0259195.00.01.474502e+12NaN0.6630.92011.0-4.0700.00.22600.004310.0000170.1010.53399.9349984.0
7405521.0La Bicicleta8.02017-01-01Shakirahttps://open.spotify.com/track/0sXvAOmXgjR2QUqLK1MltUBoliviatop200MOVE_UP3507.00sXvAOmXgjR2QUqLK1MltUEl Dorado61.0227706.00.01.495757e+12NaN0.7360.9640.0-2.1471.00.12900.198000.0000020.3360.953179.9349984.0
8405521.0La Bicicleta8.02017-01-01Carlos Viveshttps://open.spotify.com/track/0sXvAOmXgjR2QUqLK1MltUBoliviatop200MOVE_UP3507.00sXvAOmXgjR2QUqLK1MltUEl Dorado61.0227706.00.01.495757e+12NaN0.7360.9640.0-2.1471.00.12900.198000.0000020.3360.953179.9349984.0
9405524.0Closer11.02017-01-01Halseyhttps://open.spotify.com/track/7BKLCZ1jbUBVqRi2FVlTVwBoliviatop200MOVE_DOWN2898.07BKLCZ1jbUBVqRi2FVlTVwCloser86.0244960.00.01.469750e+12NaN0.7480.5248.0-5.5991.00.03380.414000.0000000.1110.66195.0100024.0
idtitlechart_rankchart_dateartisturlregioncharttrendstreamstrack_idalbumpopularityduration_msexplicitrelease_dateavailable_marketsaf_danceabilityaf_energyaf_keyaf_loudnessaf_modeaf_speechinessaf_acousticnessaf_instrumentalnessaf_livenessaf_valenceaf_tempoaf_time_signature
437151810014.0Poblado - Remix38.02021-07-31Natan & Shanderhttps://open.spotify.com/track/1WedZeiezCmCEOzLwhx0hVBoliviaviral50MOVE_DOWNNaN1WedZeiezCmCEOzLwhx0hVPoblado - Remix83.0393280.00.01.623974e+12NaN0.8130.8093.0-5.3820.00.08460.102000.0000010.37700.64693.0049974.0
437152810014.0Poblado - Remix38.02021-07-31Crissinhttps://open.spotify.com/track/1WedZeiezCmCEOzLwhx0hVBoliviaviral50MOVE_DOWNNaN1WedZeiezCmCEOzLwhx0hVPoblado - Remix83.0393280.00.01.623974e+12NaN0.8130.8093.0-5.3820.00.08460.102000.0000010.37700.64693.0049974.0
437153810014.0Poblado - Remix38.02021-07-31KAROL Ghttps://open.spotify.com/track/1WedZeiezCmCEOzLwhx0hVBoliviaviral50MOVE_DOWNNaN1WedZeiezCmCEOzLwhx0hVPoblado - Remix83.0393280.00.01.623974e+12NaN0.8130.8093.0-5.3820.00.08460.102000.0000010.37700.64693.0049974.0
437154810015.0Fulanito39.02021-07-31El Alfahttps://open.spotify.com/track/59L8x0xy8njj75vzVCPMqgBoliviaviral50MOVE_UPNaN59L8x0xy8njj75vzVCPMqgFulanito64.0158531.00.01.622678e+12NaN0.9300.8377.0-4.6320.00.18700.256000.0000020.23900.821111.8980034.0
437155810015.0Fulanito39.02021-07-31Becky Ghttps://open.spotify.com/track/59L8x0xy8njj75vzVCPMqgBoliviaviral50MOVE_UPNaN59L8x0xy8njj75vzVCPMqgFulanito64.0158531.00.01.622678e+12NaN0.9300.8377.0-4.6320.00.18700.256000.0000020.23900.821111.8980034.0
437156810018.0Love Tonight42.02021-07-31Shousehttps://open.spotify.com/track/1u73tmG4xQschbK8cXxSD9Boliviaviral50NEW_ENTRYNaN1u73tmG4xQschbK8cXxSD9Love Tonight60.0493795.00.01.513210e+12NaN0.7960.5520.0-7.2261.00.03140.001130.0101000.08760.468123.0039984.0
437157810019.0Space Song43.02021-07-31Beach Househttps://open.spotify.com/track/7H0ya83CMmgFcOhw0UB6owBoliviaviral50MOVE_DOWNNaN7H0ya83CMmgFcOhw0UB6owDepression Cherry76.0320466.00.01.440720e+12NaN0.5080.7920.0-7.3110.00.02970.229000.1240000.14500.601147.0670014.0
437158810022.0Bad Habits46.02021-07-31Ed Sheeranhttps://open.spotify.com/track/6PQ88X9TkUIAUIZJHW2upEBoliviaviral50NEW_ENTRYNaN6PQ88X9TkUIAUIZJHW2upEBad Habits85.0231041.00.01.624579e+12NaN0.8080.89711.0-3.7120.00.03480.046900.0000310.36400.591126.0260014.0
437159810025.0Casualidad49.02021-07-31Sofía Reyeshttps://open.spotify.com/track/5FcT2TuosRkokjn3xyncERBoliviaviral50NEW_ENTRYNaN5FcT2TuosRkokjn3xyncERCasualidad56.0193112.00.01.624579e+12NaN0.7530.72010.0-4.2230.00.09870.383000.0000000.05540.840173.9060064.0
437160810025.0Casualidad49.02021-07-31Pedro Capóhttps://open.spotify.com/track/5FcT2TuosRkokjn3xyncERBoliviaviral50NEW_ENTRYNaN5FcT2TuosRkokjn3xyncERCasualidad56.0193112.00.01.624579e+12NaN0.7530.72010.0-4.2230.00.09870.383000.0000000.05540.840173.9060064.0

Duplicate rows

Most frequently occurring

idtitlechart_rankchart_dateartisturlregioncharttrendstreamstrack_idalbumpopularityduration_msexplicitrelease_dateaf_danceabilityaf_energyaf_keyaf_loudnessaf_modeaf_speechinessaf_acousticnessaf_instrumentalnessaf_livenessaf_valenceaf_tempoaf_time_signature# duplicates
1768773374.0Adicto Al Dolor (Lágrimas)22.02017-04-18Don Tettohttps://open.spotify.com/track/0RH8RjiztAH4IY3iD8iuOSBoliviaviral50NEW_ENTRYNaN0RH8RjiztAH4IY3iD8iuOSLo Que No Sabias (Edicion Especial)0.0245693.00.01.208822e+120.5240.9732.0-4.5951.00.07580.0520.0000090.1260.32299.9950034.06
1771773756.0Adicto Al Dolor (Lágrimas)16.02017-04-19Don Tettohttps://open.spotify.com/track/0RH8RjiztAH4IY3iD8iuOSBoliviaviral50MOVE_UPNaN0RH8RjiztAH4IY3iD8iuOSLo Que No Sabias (Edicion Especial)0.0245693.00.01.208822e+120.5240.9732.0-4.5951.00.07580.0520.0000090.1260.32299.9950034.06
1772773830.0Adicto Al Dolor (Lágrimas)15.02017-04-20Don Tettohttps://open.spotify.com/track/0RH8RjiztAH4IY3iD8iuOSBoliviaviral50MOVE_UPNaN0RH8RjiztAH4IY3iD8iuOSLo Que No Sabias (Edicion Especial)0.0245693.00.01.208822e+120.5240.9732.0-4.5951.00.07580.0520.0000090.1260.32299.9950034.06
1773773912.0Adicto Al Dolor (Lágrimas)18.02017-04-21Don Tettohttps://open.spotify.com/track/0RH8RjiztAH4IY3iD8iuOSBoliviaviral50MOVE_DOWNNaN0RH8RjiztAH4IY3iD8iuOSLo Que No Sabias (Edicion Especial)0.0245693.00.01.208822e+120.5240.9732.0-4.5951.00.07580.0520.0000090.1260.32299.9950034.06
1774774067.0Adicto Al Dolor (Lágrimas)18.02017-04-22Don Tettohttps://open.spotify.com/track/0RH8RjiztAH4IY3iD8iuOSBoliviaviral50SAME_POSITIONNaN0RH8RjiztAH4IY3iD8iuOSLo Que No Sabias (Edicion Especial)0.0245693.00.01.208822e+120.5240.9732.0-4.5951.00.07580.0520.0000090.1260.32299.9950034.06
1777774455.0Adicto Al Dolor (Lágrimas)21.02017-04-23Don Tettohttps://open.spotify.com/track/0RH8RjiztAH4IY3iD8iuOSBoliviaviral50MOVE_DOWNNaN0RH8RjiztAH4IY3iD8iuOSLo Que No Sabias (Edicion Especial)0.0245693.00.01.208822e+120.5240.9732.0-4.5951.00.07580.0520.0000090.1260.32299.9950034.06
1782775294.0Adicto Al Dolor (Lágrimas)22.02017-04-24Don Tettohttps://open.spotify.com/track/0RH8RjiztAH4IY3iD8iuOSBoliviaviral50MOVE_DOWNNaN0RH8RjiztAH4IY3iD8iuOSLo Que No Sabias (Edicion Especial)0.0245693.00.01.208822e+120.5240.9732.0-4.5951.00.07580.0520.0000090.1260.32299.9950034.06
1783775421.0Adicto Al Dolor (Lágrimas)49.02017-04-25Don Tettohttps://open.spotify.com/track/0RH8RjiztAH4IY3iD8iuOSBoliviaviral50MOVE_DOWNNaN0RH8RjiztAH4IY3iD8iuOSLo Que No Sabias (Edicion Especial)0.0245693.00.01.208822e+120.5240.9732.0-4.5951.00.07580.0520.0000090.1260.32299.9950034.06
3408315.0Tiene Espinas El Rosal - En Vivo47.02017-09-08Grupo Cañaveral De Humberto Pabónhttps://open.spotify.com/track/5sKGs2MKATzwjsY0tRD2ZkBoliviaviral50NEW_ENTRYNaN5sKGs2MKATzwjsY0tRD2ZkJuntos Por La Cumbia (Live)45.0287746.00.01.415059e+120.5850.8484.0-3.6900.00.05110.3850.0000000.7110.749167.5209964.04
4408315.0Tiene Espinas El Rosal - En Vivo47.02017-09-08Jenny And The Mexicatshttps://open.spotify.com/track/5sKGs2MKATzwjsY0tRD2ZkBoliviaviral50NEW_ENTRYNaN5sKGs2MKATzwjsY0tRD2ZkJuntos Por La Cumbia (Live)45.0287746.00.01.415059e+120.5850.8484.0-3.6900.00.05110.3850.0000000.7110.749167.5209964.04